Carotid Artery Plaque Morphology and Composition in Relation to Incident Cardiovascular Events: The Multi-Ethnic Study of Atherosclerosis (MESA)
Bibliographic record
Abstract
PURPOSE: To determine if carotid plaque morphology and composition with magnetic resonance (MR) imaging can be used to identify asymptomatic subjects at risk for cardiovascular events. MATERIALS AND METHODS: Institutional review boards at each site approved the study, and all sites were Health Insurance Portability and Accountability Act (HIPAA) compliant. A total of 946 participants in the Multi-Ethnic Study of Atherosclerosis (MESA) were evaluated with MR imaging and ultrasonography (US). MR imaging was used to define carotid plaque composition and remodeling index (wall area divided by the sum of wall area and lumen area), while US was used to assess carotid wall thickness. Incident cardiovascular events, including myocardial infarction, resuscitated cardiac arrest, angina, stroke, and death, were ascertained for an average of 5.5 years. Multivariable Cox proportional hazards models, C statistics, and net reclassification improvement (NRI) for event prediction were determined. RESULTS: Cardiovascular events occurred in 59 (6%) of participants. Carotid IMT as well as MR imaging remodeling index, lipid core, and calcium in the internal carotid artery were significant predictors of events in univariate analysis (P < .001 for all). For traditional risk factors, the C statistic for event prediction was 0.696. For MR imaging remodeling index and lipid core, the C statistic was 0.734 and the NRI was 7.4% and 15.8% for participants with and those without cardiovascular events, respectively (P = .02). The NRI for US IMT in addition to traditional risk factors was not significant. CONCLUSION: The identification of vulnerable plaque characteristics with MR imaging aids in cardiovascular disease prediction and improves the reclassification of baseline cardiovascular risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".